An Algorithm for Assessing The Competitiveness of Tourism Enterprises: A Benchmarking-Based Integral Index Approach

Destination-level competitiveness research has produced a rich set of macro- and mezo-level composite indicators, yet the firm — the unit that ultimately delivers the visitor experience — remains comparatively under-instrumented in the tourism competitiveness literature. This article develops and formalizes a micro-level algorithm for assessing the competitiveness of individual tourism enterprises, implemented in a purpose-built software tool, “BoburCalculator.” The algorithm rests on two operations applied uniformly across five enterprise modules — hotels, tour operators, restaurants and cafés, transport companies, and other tourism-related firms: benchmark normalization, in which each raw indicator is rescaled against the best-performing firm in the comparison set, and weighted integral aggregation, in which four module-specific normalized criteria are combined into a single competitiveness score bounded on [0,1]. For each module the article specifies the raw indicators (profitability, market share, occupancy, customer satisfaction, loyalty, digital-order share, human-capital quality, punctuality, safety, and capacity utilization, among others), the corresponding normalization formulas, and the resulting integral index. The transport module additionally incorporates an inverse (“fewer incidents is better”) normalization for safety, illustrating that the benchmarking logic generalizes to indicators with either monotonic direction. The algorithm's structure is presented in both a staged and a modular flowchart implementation. The approach is positioned relative to composite-indicator methodology (Nardo et al., 2005; OECD/JRC, 2008; Saisana & Tarantola, 2002), benchmarking theory (Camp, 1989), and firm-level competitiveness frameworks (Man, Lau & Chan, 2002; Ambastha & Momaya, 2004), and is shown to complement, rather than duplicate, the macro- and mezo-level tourism competitiveness indices developed elsewhere in this dissertation. The article concludes with a discussion of weight-elicitation options (expert judgment, AHP), data-quality prerequisites, and the algorithm's intended use as a decision-support and benchmarking tool for tourism enterprises and sectoral regulators in Uzbekistan.